Computer processing techniques for streak reduction in computed tomography images

ABSTRACT

Computer processing techniques are described for reducing streaks in computed tomography (CT) images. According to an embodiment, computer-implemented method comprises obtaining, by a system comprising a processor, a pair of CT images reconstructed from a same set of projection data, the pair comprising a first image reconstructed from the projection data using a standard reconstruction process and a second image reconstructed from the projection data using a filtering reconstruction process that results in the second image comprising a first reduced level of streaks relative to the first image. The method further comprises generating, by the system, a third image by fusing a first subset of pixels extracted from one or more non-uniform areas in the first image and a second subset of pixels extracted from one or more uniform areas in the second image, wherein the third image comprises a second reduced level of streaks relative to the first image.

TECHNICAL FIELD

This application relates to medical image processing and moreparticularly to computer processing techniques for reducing streaks incomputed tomography (CT) images.

BACKGROUND

Computed tomography (CT) has been one of the most successful imagingmodalities and has facilitated countless image-based medical proceduressince its invention decades ago. CT scanners use a rotating X-ray (XR)tube and a row of detectors placed in the gantry to measure XRattenuations by different tissues inside the body. The multiple XRmeasurements taken from different angles are then processed on acomputer using reconstruction algorithms to produce tomographic(cross-sectional) images (reconstructed “slices”) of the body. As the XRtube rotates around the body of the patient, the XR pathlength throughthe patient can be different at different tube locations. There is moreXR attenuation when the XR pathlength is longer and thus less XR photonsarrive at the detector for longer pathlengths relative to shorterpathlengths. This results in increased noise on XR signal projectionswith longer pathlengths compared to shorter pathlengths. The higheramounts of noise in the XR projections associated with longerpathlengths result in streaks in the reconstructed CT image.

One of the primary mechanisms for minimizing streaks attributed tolonger pathlengths involve tube current modulation (TCM), wherein thetube current is increased for longer pathlengths and decreased forshorter pathlengths. However, TCM is limited by the hardware capacity ofthe CT scanner system at hand. Some CT scanners may not have thecapability to adjust the tube current for different tube positionsand/or may not have the capacity to increase the tube current highenough for longer pathlengths to obtain projections with sufficientlyreduced noise to minimize corresponding streaks in the reconstructedimages. In addition, increasing the tube current for longer pathlengthsincreases the amount of ionizing radiation exposure to the body, whichcan be harmful over time. Accordingly, techniques for minimizing streaksin CT images that do not suffer from these hardware restraints areneeded.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements or delineate any scope of thedifferent embodiments or any scope of the claims. Its sole purpose is topresent concepts in a simplified form as a prelude to the more detaileddescription that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products are described that provide computerprocessing techniques for reducing streaks in computed tomography (CT)images.

According to an embodiment, a system is provided that comprises a memorythat stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components comprise an image processing componentthat obtains a pair of CT images reconstructed from a same set ofprojection data, the pair comprising a first image reconstructed fromthe projection data using a standard reconstruction process and a secondimage reconstructed from the projection data using a filteringreconstruction process that results in the second image comprising afirst reduced level of streaks relative to the first image. The computerexecutable components further comprise a fusion component that generatesa third image by fusing a first subset of pixels extracted from one ormore non-uniform areas in the first image and a second subset of pixelsextracted from one or more uniform areas in the second image, whereinthe third image comprises a second reduced level of streaks relative tothe first image. In one or more embodiments, the fusion component candetermine noise level measures for each pixel of the second image andidentify the first subset of pixels and the second subset of pixelsbased on the noise level measures and one or more defined thresholdvalues for the noise level measures of the second subset.

In some embodiments, the standard reconstruction process comprisesreconstructing the first image from the projection data withoutprefiltering the projection data and wherein the filteringreconstruction processes comprises filtering the projection data intoprefiltered projection data and reconstructing the second image from theprefiltered projection data, wherein the prefiltered projection datacomprises projections with reduced noise as function of pathlengths ofthe projections or function of a combination of the pathlengths and tubecurrent, and wherein an amount of the reduced noise increases as thepathlengths increase or the tube current decreases. In someimplementations of these embodiments, the (original) projection data canbe obtained by the image processing component. In other embodiments, theoriginal projection data may not be provided. With these embodiments,the computer executable components can further comprise a projectioncomponent that estimates the projection data from the first image usinga forward image projection process, resulting in simulated projectiondata, and wherein the filtering reconstruction processes comprisesfiltering the simulated projection into the prefiltered projection dataas opposed to the filtering the projection data. The computer executablecomponents can further comprise a projection space filtering componentthat generates the prefiltered projection data from the projection dataor the simulated projection data. To facilitate this end, the projectionspace filtering component estimates the pathlengths of the projectionsusing a projection smoothing process, estimates noise levels of theprojections based on the pathlengths or a combination of the pathlengthsand tube current, and determines the amount of the reduced noise for theprojections based on the noise levels. The computer executablecomponents can further comprise a reconstruction component thatgenerates the second image from the prefiltered projection data. Thereconstruction component may also generate the first image from the(original) projection data.

In other embodiments, the filtering reconstruction process comprises animage space filtering processes that is used to generate the secondimage by filter processing the first image. With these embodiments, thecomputer executable components further comprise an image space filteringcomponent that performs the image space filtering process on the firstimage to generate the second image.

In one or more additional embodiments, the computer executablecomponents can further comprise a training component that receives aplurality of pairs of CT images respectively comprising original imagesgenerated using the standard reconstruction process and fused imagescorresponding to the third image generated by the fusion component. Withthese embodiments, the training component can train an imagetransformation model comprising a neural network to transform theoriginal images into corresponding versions of the fused images,resulting in a trained image transformation model. The computerexecutable components can further comprise an inferencing component thatreceives a new CT image reconstructed from a new set of projection datausing the standard reconstruction process and generates an optimizedimage with a reduced level of streaks relative to the new CT imagesusing the trained image transformation model. In some implementations ofthese embodiments, the computer executable components further comprise atraining data preparation component that determines streak data for eachpair of the plurality of pairs based on differences between the originalimages and the fused images, and the training component trains theneural network to estimate the streak data given the original images asinput, resulting in estimated streak data. With these implementations,the image transformation model can be configured to remove the estimatedstreak data from the original images to generate the correspondingversions of the fused images.

In some embodiments, elements described in the disclosed systems andmethods can be embodied in different forms such as acomputer-implemented method, a computer program product, or anotherform.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates pathlength-based streak generation in an examplecomputed tomography (CT) image in accordance with one or moreembodiments of the disclosed subject matter.

FIG. 2 presents a high-level flow diagram of an examplecomputer-implemented process for reducing streaks in a CT image inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 3 presents an example computing system that facilitates reducingstreaks in CT images in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 4 presents a flow diagram of an example computer-implementedprocess for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter.

FIG. 5 illustrates forward projection of a CT image to obtain simulatedprojection data in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 6 presents a flow diagram of another example computer-implementedprocess for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter.

FIG. 7 presents a flow diagram of another example computer-implementedprocess for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter.

FIG. 8 presents another example computing system that facilitatesreducing streaks in CT images in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 9 presents a flow diagram of an example computer-implementedprocess for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 10 presents a flow diagram of another example computer-implementedprocess for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 11 presents a flow diagram of another example computer-implementedprocess for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 12 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background section,Summary section or in the Detailed Description section.

The disclosed subject matter is directed to systems,computer-implemented methods, apparatus and/or computer program productsthat provide computer processing techniques for reducing streaks in CTimages. The disclosed techniques aim to reduce streaks in a CT imagewhile maintaining spatial resolution and structure details in an imageby adaptively blending selected pixels from two CT images reconstructedfrom the same set of original captured XR projection data in differentmanner. In this regard, one of the two images is generated from theoriginal projection data using a standard CT image reconstructionprocedure that results in the image comprising streaks in at least someareas associated with longer projection pathlengths. The other image isprocessed using a filtering reconstruction process that aims to suppressnoise attributed to projections with longer pathlengths. The secondimage generated using the filtering reconstruction process has fewerstreaks relative to the first image, yet some blurring or reducedresolution effects attributed to the noise reduction. Differentfiltering reconstruction processes are described herein for generatingthe second image.

The disclosed techniques further extract a first subset of pixels fromthe first image from non-uniform areas (e.g., areas where higherresolution pixels are needed for more detailed structures, such as areasassociated with bones, organs, target tissues, etc.) and a second subsetof pixels from the uninform areas in the second image. The disclosedtechniques further employ an adaptive blending process that comprisesfusing the first and second subsets of pixels to generate a third andfinal CT image (referred to herein as a fused or blended image) that hasa reduced level of streaks relative to the first image in the uniformareas yet preserves the resolution of the first image in the non-uniformareas.

The disclosed subject matter further provides techniques for reducingstreaks in CT images using principles of artificial intelligence (AI)and machine learning (ML). In this regard, in one or more additionalembodiments, the methods for generating fused images described above canalso be used to prepare a training data set for training a deep neuralnetwork model to generate an optimized image corresponding to the fusedimage described above given an original CT image (e.g., processed usinga standard reconstruction processes) as input. The training datasetcomprises pairs CT images, wherein each pair comprises an original CTimage reconstructed using the standard reconstruction process (e.g.,corresponding to the first image above) and a fused CT imagecorresponding to the third image described above generated using theadapted blending techniques disclosed herein. In some embodiments, thetraining process comprises training the neural network to transform theoriginal CT images into corresponding versions of their paired fusedimages. Additionally, or alternatively, the training process cancomprise training the neural network to estimate streak data included inthe original CT images, wherein the streak data used for the trainingprocess can be calculated based on difference between the original CTimages and their paired fused images. With these embodiments, the outputof the trained neural network comprises predicted streak data for agiven original CT image. After the network has been trained, the trainedneural network can be applied to a new original CT image (e.g., withstreaks) to generate estimated streak data for the new CT image. Thedisclosed techniques can further generate a final optimized image byremoving the estimated streak data (output by the neural network model)from the original CT image.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

FIG. 1 illustrates pathlength-based streak generation in an examplecomputed tomography (CT) image 100 in accordance with one or moreembodiments of the disclosed subject matter. The example CT image 100illustrated in FIG. 1 is an axil cross-sectional image of a patient'sabdomen depicting a view of the patient's liver 102 and heart 104. TheCT image 100 is a standard CT image reconstructed using a conventionalor standard CT image reconstruction process from original CT scanprojection data captured using a conventional CT scanning procedure. Inaccordance with conventional CT scanning procedures, the patient iscentered on a motorized scanning table through a circular opening in theCT imaging system. As the patient passes through the CT imaging systemvia the motorized scanning table (e.g., incrementally forward orbackward to generate different tube positions relative to the body ofthe patient) an XR tube providing a source of XRs rotates around theinside of the circular opening. The XR tube produces a narrow,fan-shaped beam of XR used to irradiate a section of the patient's body.In typical examinations there are several phases; each made up of 10 to50 rotations of the XR tube around the patient in coordination with thetable moving through the circular opening. Detectors on the exit side ofthe patient record the XR signals exiting the section of the patient'sbody being irradiated as an XR “snapshot” at one position (angle orview) of the XR tube. More particularly, the CT scanner uses an XR tubethat projects XR photons through the body of the patient and an array ofdetectors placed in the gantry on the opposite side of the body tomeasure XR attenuations of the photons by different tissues inside thebody based on the amount of photons arriving at the detector cells.Thus, each “snapshot” corresponding to each tube position can comprise aplurality of projections. The number of projections obtained for eachtube position (e.g., each tube angle or view) depends on configurationof the detector cells, which can include an array of one or more rowsand columns of cells. Many different “snapshots” (angles) are collectedduring one complete (e.g., 360°) rotation. The collection of projectionscaptured during the scanning process is then sent to a computer toreconstruct all of the individual “snapshots” into a cross-sectionalimage (slice) of the internal organs and tissues for each completerotation of the XR tube. In this regard, for conventional (e.g.,non-helical) CT scanning procedures, the collection of signals (referredto herein as projections) captured at each tube position (e.g., angle orview) for a single rotation are then back projected to create thereconstructed image corresponding to a cross-sectional slice of the bodyfor that rotation.

As the XR tube rotates around the body of the patient, the XR pathlengthbetween the tube and the detector can be different at different tubepositions. There is more XR attenuation when the XR pathlength is longerand thus less XR photons arrive at the detector for longer pathlengthsrelative to shorter pathlengths. This results in increased noise on XRsignal projections with longer pathlengths compared to shorterpathlengths. The higher amounts of noise in the XR projectionsassociated with longer pathlengths result in streaks in thereconstructed CT image. For example, as illustrated in FIG. 1 , thesolid arrow line corresponds to a projection with a relatively longpathlength (e.g., the length between XR tube and the detector) and thedashed arrow line correspond to a projection with a shorter pathlength.The projection corresponding to the solid arrow line has higher noiserelative to the projection along the dashed arrow line due to lessphotons arriving at the detector. As a result, streaks are present inthe reconstructed CT image 100 along the solid arrow line.

The disclosed subject matter is directed to systems,computer-implemented methods, apparatus and/or computer program productsthat provide computer processing techniques for reducing such streaks inCT images attributed to longer pathlengths. The disclosed techniques aimto reduce streaks in a CT image while maintaining spatial resolution andstructure details in an image by adaptively blending selected pixelsfrom two CT images reconstructed from the same set of original capturedXR projection data in different manner.

FIG. 2 presents a high-level flow diagram of an examplecomputer-implemented process 200 for reducing streaks in a CT image inaccordance with one or more embodiments of the disclosed subject matter.In accordance with process 200, at 202 a system comprising a processor,(e.g., system 300 and/or system 800 described infra), obtains a pair ofCT images reconstructed from a same set of projection data, the paircomprising a first image reconstructed from the projection data using astandard reconstruction process and a second image reconstructed fromthe projection data using a filtering reconstruction process thatresults in the second image comprising a first reduced level of streaksrelative to the first image. As described in great detail infra, in someembodiments, the system can receive or otherwise obtain the originalprojection data and perform the standard reconstruction process togenerate the first image using the original projection data. The systemcan also perform the filtering reconstruction process using the originalprojection data to generate the second image. With these embodiments,the filtering reconstruction process can comprise a projection spacefiltering process that involves filtering the original projection databased on respective pathlengths associated with the projections togenerate filtered projection data and reconstruct the second image fromthe filtered projection data. In other embodiments, the system mayreceive the first image as already reconstructed using the standardreconstruction process and perform the filtering reconstruction processstarting from the first image to generate the second image. In someimplementations of these embodiments, this can involve forwardprojecting the first image to obtain simulated projection data for thefirst image and performing the projection space filtering process usingthe simulated projection data. In other implementations, the system canemploy a different filtering reconstruction process referred to as animage space filtering reconstruction process to generate the secondimage from the first image. These filtering reconstruction processes aredescribed in greater detail infra.

At 204, process 200 further comprises generating, by the system, a thirdimage by fusing a first subset of pixels extracted from one or morenon-uniform areas in the first image and a second subset of pixelsextracted from one or more uniform areas in the second image, whereinthe third image comprises a second reduced level of streaks relative tothe first image. In his regard, the system performs an adaptive blendingprocess that involves blending one or more portions of the first image,the original CT image having a higher resolution yet streaks, with oneor more portions of the second image, the filtered image with reducednoise and thus less streaks yet some blurring effect. As a result, thethird image, referred to herein as the “fused image” or “blended image,”has a reduced level of streaks relative to the first image in theuniform areas yet preserves the resolution of the first image in thenon-uniform areas. The portions used from the first image can includethose where higher resolution pixels are needed for more detailedstructures, such as areas associated with bones, organs, target tissues,etc., while the portions used from the second image include those wherelower resolution pixels are sufficient, such as those associated withuniform areas depicting less detailed anatomical structures. Forexample, with reference back to FIG. 1 , the uniform areas in theexample CT image 100 may correspond to the areas defined by the liver102 and the heart 104 while the non-uniform areas may include the otheranatomical structures excluding the liver 102 and the heart 104.Techniques for identifying and selecting the specific subsets of pixelsto be extracted from the first image and the second image are describedin detail infra.

FIG. 3 presents an example computing system 300 that facilitatesreducing streaks in CT images in accordance with one or more embodimentsof the disclosed subject matter. Embodiments of systems described hereincan include one or more machine-executable components embodied withinone or more machines (e.g., embodied in one or more computer-readablestorage media associated with one or more machines). Such components,when executed by the one or more machines (e.g., processors, computers,computing devices, virtual machines, etc.) can cause the one or moremachines to perform the operations described.

In this regard, computing system 300 provides an example computingsystem comprising a computing device 308 that includesmachine-executable components configured to perform one or more of theoperations described in process 200 and additional processes describedherein. The computer executable components include an image dataprocessing component 310 configured to receive and process original CTimage data 306 into fused CT image data 328 using various computerexecutable sub-components, including projection space filteringcomponent 312, reconstruction component 314, fusion component 316, imagespace filtering component 318 and projection component 320. Thesecomputer/machine executable components (and other described herein) canbe stored in memory associated with the one or more machines. The memorycan further be operatively coupled to at least one processor, such thatthe components can be executed by the at least one processor to performthe operations described. For example, in some embodiments, thesecomputer/machine executable components can be stored in memory 324 ofthe computing device 308 which can be coupled to processing unit 322 forexecution thereof. The computing device 308 further include a device bus326 that communicatively and operatively couples image data processingcomponent 310 and its sub-components (e.g., the projection spacefiltering component 312, the reconstruction component 314, the fusioncomponent 316, the image space filtering component 318 and theprojection component 320) to the processing unit 322 and the memory 324.Examples of said and memory 324 and processing unit 322 as well as othersuitable computer or computing-based elements, can be found withreference to FIG. 12 , and can be used in connection with implementingone or more of the systems or components shown and described inconnection with FIG. 3 or other figures disclosed herein.

The original CT image data 306 can comprise original (raw orunprocessed) CT projection data captured via a CT scanner 304 inaccordance with a CT scan of an anatomical region of a patient. Theoriginal CT image data 306 may also include one or more CT imagesreconstructed from the original CT projection data using a standard orconventional CT image reconstruction process (e.g., a reconstructionprocess other than the filtering reconstruction processes disclosedherein). In this regard, in some embodiments, the original CT image data306 can comprise the projection data and/or the first image describedwith referenced to process 200 and the fused CT image data cancorrespond to the third image described with reference to process 200.In some embodiments, the image data processing component 310 can receivethe original CT image data 306 directly from the CT scanner 304 and/oras stored in a medical image database 302. The computing device 308 canfurther be communicatively and operatively coupled to the CT scanner 304and/or the medical image database 302 via one or more wired or wirelesscommunication networks. In other embodiments, the original CT image data306 may be stored locally in memory 324. In this regard, the deploymentarchitecture of computing system 300 can vary. In some embodiments, thecomputing device 308 can correspond a single computing device (e.g.,real or virtual). In other embodiments, the computing device 308 cancorrespond to two or more separate communicatively coupled computingdevices operating in a distributed computing environment. Variousalternative deployment architecture variations can also be used.

With reference to FIGS. 2 and 3 , the projection space filteringcomponent 312 can perform a projection space filtering process using theoriginal CT image data 306 to generate filtered projection data. Thereconstruction component 314 can further processes the filteredprojection data to generate or reconstruct the second image describedwith reference to process 200. In implementations in which the originalCT image data 306 does not include a previously reconstructed originalCT image corresponding to the first image in process 200, thereconstruction component 314 can also perform a standard CT imagereconstruction process using the original CT projection data (includedin the original CT image data 306) to generate the first image.Additionally, or alternatively, the image space filtering component 318can perform an image space filtering process using the original CT imagedata 306 to generate the second image described with reference toprocess 200. The fusion component 316 can further perform the adaptiveblending of the first and second images to generate the fused CT imagedata 328 corresponding to the third image in process 200.

The projection space filtering process performed by the projection spacefiltering component 312 involves filtering an original set of projectiondata (e.g., native, as captured by the CT scanner 304) based onrespective pathlengths (l) of the projections to remove noise associatedwith the respective projections, wherein the amount of noise removedincreases as the pathlength increases. In this regard, the original setof projection data can comprise an original set or group of projectionsignals captured during a CT scan that is used to create a singletwo-dimensional (2D) image corresponding to a cross-sectional image ofthe patient's body. For example, as applied to conventional CT scanning,the set of projection data (P) can comprise the complete set ofprojections captured during a single rotation of the XR tube around thepatient. It should be appreciated that the filtering process discussedherein can be applied filter multiple sets of projection data (P)captured during different entire rotations of the CT scanning procedure(e.g., all captured projection data at different tube rotationscorresponding to different cross-sectional regions of the patient). Thefiltering processes discussed herein can also be applied to projectiondata captured in association with helical scanning and multi-slicescanning. With these embodiments the set of projection data (P) used tocreate a single 2D image can include projections associated with helicaland partial rotations of the gantry.

Each set of projection data (P) comprises a plurality of subsets ofprojection data, one for each view or tube position/angle within therotation. As noted above, the pathlength through the patient can varyfor each view. Each subset of projection data can further be defined bya plurality of individual projections (p) corresponding to the signalsreceived at individual detector cells of the detector used, which may bedefined by one or more rows and one or more columns of detector cells(i.e., a detector array). In some implementations, the pathlength canalso vary within a same view for each detector cell channel, wherein thechannel corresponding to a specific column (col) and row (col, row). Inthis regard, each individual projection (p) included in the set ofprojection data (P) may be defined by a view, a detector column (col),and a detector row (row) as follows: p(col, row, view). At a high level,the projection space filtering processes comprises filtering the set ofprojection data P to create filtered projections p_(f) with similarnoise levels (e.g., within a defined degree of deviation) for each viewas a function of the pathlength associated with each projection p,wherein the pathlength (l) can vary per view and detector cell row andcolumn, and wherein the amount of noise filtered out of the projectionsp using the filtering function F increases as the path length increases.The pathlength (l) for each detector cell and view is estimated (asdescribed in greater detail below). In other words, the projections pare filtered using a filtering function F to reduce or remove the noiseon the projections with longer pathlengths to be similar (e.g., relativeto a defined degree of deviation) to the noise on the projections withshorter pathlengths. Expressed mathematically, in some embodiments, thefiltered projections pf can be defined by Equation 1 below:

p _(f)(col,row,view)=_(F)(p(col,row,view),pathlength(col,row,view))  Equation 1.

In some embodiments, the projection space filtering process can alsofilter the projection data as a function of a combination of therespective pathlengths of the projections and tube currents of therespective projections. The tube current is typically measured inmilliampere-seconds, also more commonly known as mAs, and is a measureof radiation produced (milliamperage) over a set amount of time(seconds) via an x-ray tube. In this regard, some CT scanning systemsare capable of adjusting the tube current the XR tube (i.e., the XRphoton source) at different tube angles (e.g., views) and positionsrelative to the patient the course of a CT scan, a process known as tubecurrent modulation. TCM may be used to increase the tube current (i.e.,the number of x-ray photons) for longer pathlengths and thicker bodyregions and decrease the tube current for shorter pathlengths andthinner body regions. As patients are not homogeneous cylinders, the endresult is typically that the tube current oscillates up and down withina single rotation of the gantry, and increases, on average, throughthick body regions (e.g., the shoulders and hips), and decreases, onaverage, through thinner body regions (e.g., the chest). In this regard,the TCM can be adjusted to dynamically control the number of photons atevery projection through the patient. As described in the Backgroundsection, increasing the tube current for longer pathlengths can increasethe number of photons arriving at the detector cells and thus decreasethe amount of corresponding noise in the projections to at least somedegree. The TCM used thus significantly impacts radiation dose andcorresponding image quality and can vary rapidly across multiple CTacquisition rotations as well as within a single rotation of the gantry.With these embodiments, the specific tube current mA used for each viewcan also be factored into the projection space filtering equation, inaccordance with Equation 2 below:

p_(f)(col,row,view)=F(p(col,row,view),pathlength(col,row,view),mA(view))  Equation 2.

In accordance with Equations 1 and 2 above and the image space filteringprocesses, the original set of projection data (P) can be provided withthe with original CT image data 306. The original set of projection dataP includes information defining the projection p values for each view,that is, each (p(col,row,view). In implementations in which differentmAs are used for different views, the original projection data will alsoinclude the tube current mA values used for each view; mA(view). Theprojection space filtering processes performed by projection spacefiltering component 312 involves estimating the pathlengths for eachprojection p using a projection smoothing process, estimating the noiselevels of the projections based on the pathlengths or a combination ofthe pathlengths and the tube current, determining amounts of the noisereduction (e.g., the F function values or filter strength) based on thenoise levels, and generating the prefiltered projections (p_(f)) byremoving the amounts of reduced noise from the projections p inaccordance with Equations 1 or 2 above. In this regard, the projectionspace filtering processes results in transformation of the original setof projection data P into a set of prefiltered projection data P_(f)that comprises filtered projections p_(f)(col,row,view) for at leastsome (one or more) of the different views and/or channels. In thisregard, it should be appreciated that in some implementations, originalprojections p associated with relatively short pathlengths may not befiltered.

Additional information regarding each of these steps of the projectionspace filtering processes are now described. In one or more embodiments,the projection space filtering process can initially involve apre-smoothing process wherein the original projections p arepre-smoothed to reduce noise and to obtain an estimation of thepathlength l associated with each projection in accordance with Equation3.

$\begin{matrix}{{l\left( {{col},{row},{view}} \right)} = {\frac{p_{s}\left( {{col},{row},{view}} \right)}{\mu_{w}}.}} & {{Equation}3}\end{matrix}$

In accordance with Equation 3, the projection space filtering component312 can compute the pathlength l for each pre-smoothed projection p_(s)defined by a specific view (i.e., angle/position of the XR tube),detector column, and detector row (i.e., (col,row,view)) using the watattenuation coefficient and μ_(w) is the water attenuation coefficient.Since μ_(w) is a constant value, p_(s)(col,row,view)/μ_(w) can be usedas an estimation of the pathlength l for each pre-smoothed projectionp_(s)(col,row,view).

Once the estimated pathlengths l for each pre-smoothed projectionp_(s)(col,row,view) have been determined, the projection space filteringcomponent 312 can estimate the noise level σ(col,row,view) associatedwith each projection p(col,row,view) based on its pathlength. In one ormore embodiments, this can be achieved using a sigmaestimate functionand predetermined reference information for the CT imaging system fromwhich the original projection data P was obtained that maps differentpathlengths and tube current values mA to estimated noise levels. Thispredetermined reference information can be generated using phantomstudies and stored in memory 324 (or another suitable memory storage) inthe form of a look-up table. In particular, the noise level estimatesare determined for each channel (e.g., each (col, row) cross views. Tofacilitate the reconstruction, a segment of projections can be used, andthe starting view and end view is V_(s) and V_(e). The noise levels canbe estimated based the pre-smoothed projection p_(s) in accordance withEquation 4 or the pathlength l in accordance with Equation 5. To thisend, lowest low noise level for channel (col, row) cross viewσ_(low)(col,row) is estimated.

σ(col,row,view)=sigmaestimate(p _(s)(col,row,view),mA)   Equation 4.

σ(col,row,view)=sigmaestimate(l(col,row,view),mA)   Equation 5.

The sigmaestimate function in the former sub step can be pre-constructedfor the CT imaging system using phantom studies. In particular, aphantom object can be scanned using the CT imaging system multiple timesat for each mA setting of a plurality of different mA settings. Forexample, at a first mA setting, the phantom can be scanned multipletimes to obtain multiple measurements for each path length p(l,i),wherein l is the path length and i∈[1 m] is the noise on level on pathlength l, which can be calculated with the standard deviation forstd(p(l,i)) i∈[1 m]. After the noise level for each pathlength isacquired at this first mA, the same processes can be repeated atdifferent mAs to obtain noise level measurement for each pathlength atdifferent mAs.

After the noise levels are estimated for each pathlength and mA usingthe phantom, a look up-table can be formed which can be used to estimatethe noise level a on projections p and/or the pre-smoothed projectionsp_(s) based on their estimated pathlengths l. The tube output can alsobe factored into the equation and scaled based on air calibration data.In particular, the projection space filtering component 312 cancalculate the estimated noise level σ_(p)(col,row,view) for eachprojection p(col,row,view) with the sigmaestimate function formed usingthe phantom and the look-up table.

Once the noise levels are estimated for each projection, the projectionspace filtering component 312 can filter the projections with kernel Kbased on the lowest noise level for each channel and/or view to make allthe projections associated with each channel and/or view have a noiselevel that is the same or substantially similar (e.g., within a defineddegree of deviation) to the lowest noise level for that channel and/orview, (noting again that each view includes a subset of projections withdifferent path lengths and noise levels). This means that for eachchannel and/or view, the noise of the corresponding filtered projectionsshould be closest to the lowest noise level for that channel/view. Thiscan be expressed mathematically as follows: filter the p(col,row,view)with kernel K to reduce estimated noise level σ_(p)(col,row,view) to beclose to σ_(low)(col,row), in accordance with Equation 6, T is apredefined threshold parameter, and when σ_(p)(col,row,view) iscontrolled under the predefined range T, streaks are largely reduced ornot visible.

σ_(p)(col,row,view)/σ_(low)(col,row)<T   Equation 6.

In this regard, K corresponds to a convolutional kernel. The strength ofthe K used is directly proportional to the degree of the estimatednoise. That is, when the noise associated with a projection is high, astronger filtering kernel K is used, and vice versa.

The convolution kernel K is determined by the following method. Theconvolution kernel K is a m*n matrix, all elements are in K smaller orequal to 1. The sum of all elements in K is one. The center value of Kis the largest value in K. the noise reduction ratio r can be calculatedin accordance with Equation 7.

$\begin{matrix}{r = {{{sqrt}\left( {\sum\limits_{{i = 1},{j = 1}}^{{i = m},{j = n}}{K\left( {i,j} \right)}^{2}} \right)}.}} & {{Equation}7}\end{matrix}$

A projection p(col,row,view) with noise level σ(col,row,view) isconvoluted with K, and the new noise level σ_(p)(col,row,view) can becalculated using Equation 8.

σ_(p)(col,row,view)=r*σ(col,row,view)   Equation 8.

A set of K can be formed, and r can be pre calculated. In the formerstep, we require σ_(p)(col,row,view)/σ_(low)(col,row)<T, then a desirednoise level after filtering is σ_(low)(col,row)*T and the desired ratior=σ(col,row,view)/σ_(low)(col,row)*T, with the r, we can find predefinedkernel K and use K filter projection p(col,row,view).

As noted above, the projection space filtering processes results intransformation of the original set of projection data P into a set ofprefiltered projection data P_(f) that comprises filtered projectionsp_(f)(col,row,view) with removed noise for at least some (one or more)of the different views and/or channels.

In one or more embodiments, after the prefiltered projection data P_(f)has been generated, the reconstruction component 314 can generate orreconstruct a CT image using the prefiltered projection data P_(f). Thisreconstructed image has a reduced level of streaks due to the reducednoise in the projection data, yet some blurring or reduction inresolution. This reconstructed image is generated from P_(f) is thusreferred to herein as the “streak free” image or I_(s). Thisreconstructed image I_(s) corresponds to the second image described inprocess 200. The reconstruction component 314 can also reconstructanother CT image using the original projection data P (the unfilteredprojection data). This reconstructed image corresponds to the firstimage in process 200 and is referred to herein as image I.Alternatively, the original CT image I may be included in the originalCT image data 306.

The fusion component 316 can further perform an image blending or fusionof image I_(s) image I to generate a new, fused image (e.g., the fusedCT image data 328) that contains subsets of pixels from each of the twoimages. In particular, the fusion component 316 can identify and extractfirst subset of pixels from image I corresponding to one or morenon-uniform areas in the first image I and second subset of pixelscorresponding to one or more uniform areas in streak free image I_(s).The fusion component 316 can further generate the new, fused image byfusing the first subset of pixels with the second subset of pixels. Inone or more embodiments, to facilitate this end, the fusion component316 can identify the first subset of pixels and the second subset ofpixels based on the noise level measures determined for each of thepixels in the streak free image I_(s) and one or more defined thresholdvalues for the noise level measures of the second subset. In particular,the fusion component 316 can compute the standard deviation (STD) foreach pixel in the streak free image I_(s). The fusion component 316 canfurther compute a histogram of the STD values and use the bin value withthe maximum number of pixels as the noise level (NL). The fusioncomponent can further generate the new fused image (newimg(i,j) based onthe STD values, the noise levels NL and a defined scaling factor sf inaccordance with Equation 9.

$\begin{matrix}{{{newimg}\left( {i,j} \right)} = \left\{ {\begin{matrix}{I\left( {i,j} \right)} & {{{if}{{STD}\left( {i,j} \right)}} > {{sf}*{NL}}} \\{I_{s}\left( {i,j} \right)} & {{{if}{{STD}\left( {i,j} \right)}} \leq {NL}} \\{{r*{I\left( {i,j} \right)}} + {\left( {1 - r} \right)*{I_{s}\left( {i,j} \right)}}} & {r = {\frac{{{STD}\left( {i,j} \right)} - {NL}}{\left( {{sf} - 1} \right){NL}}{otherwise}}}\end{matrix}.} \right.} & {{Equation}9}\end{matrix}$

In this regard, the fusion component 316 can compute the local STD foreach pixel in the streak free image I_(s) and calculate the noise levelfor the whole image I_(s) based on the histogram. For most CT imagesthere is a lot of uniform area, so the peak of the STD histogram will bethe high noise level values associated with non-uniform areas, suchvessels and bones (which will most often be a smaller representation ofthe entire image), and the noise levels in the uniform areas will becomparatively low. In accordance with Equation 9, if the local STD of apixel is larger than the scaling factor sf, this indicates the pixel isassociated with a non-uniform area, and the fusion component 316 canextract the corresponding pixel from the original image I. Likewise, ifthe local STD of a pixel is smaller than the scaling factor sf, thisindicates the pixel is associated with a uniform area, and the fusioncomponent 316 can extract the corresponding pixel from the originalimage I_(s). In some embodiments, (and as defined by Equation 9), if thelocal STD of a pixel is near scaling factor sf or withing a definedrange of the scaling factor sf (e.g., indicating the pixel is near orincluded in a partially uniform area and a partially non-uniform area),the fusion component 316 can combine perform a linear combination of theassociated pixels from both images. The new fused image will be thefinal output image and be substantially streak free and sharp. Inaccordance with system 300, new, fused images generated by the fusioncomponent 316 are represented by the fused CT image data 328.

FIG. 4 presents a flow diagram of an example computer-implementedprocess 400 for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter. Process 400corresponds to an example process that may be performed by the imagedata processing component 310 of system 300 using the projection spacefiltering process described above. Repetitive description of likeelements employed in respective embodiments is omitted for sake ofbrevity.

With reference to FIGS. 3 and 4 , in accordance with process, theprojection filtering component 312 obtains original projection data 402(P) and performs the projection space filtering processes at 404 tofilter the original projection data 404 into prefiltered projection data(P_(f)) 406. For example, the original projection data (P) can beincluded in the original CT image data 306 and comprise a raw (e.g.,unfiltered) set of projection CT data corresponding to a single 2D CTimage. As described above, the projection space filtering processcomprises estimating the pathlengths for each of the projections (p),determining the noise levels associated with each of the projectionsbased on the estimated pathlengths or a combination of the pathlengthsand the tube current mA values associated with each of the projections(e.g., using a predefined look-up table generated using the phantomstudy as described above), and filtering the projections to remove noiseusing a filter value/strength that increases relative to the noiselevels.

At 408, the reconstruction component 314 can reconstruct a first CTimage 410 (e.g., the streak image I) from the original projection data Pand a second CT image 412 (e.g., the streak free image I_(s)) from theprefiltered projection data (P_(f)). At 414, the fusion component 316can identify and extract a first subset of pixels 416 from one or morenon-uniform areas in the first CT image 410 and a second subset ofpixels 418 from one or more uniform areas in the second CT image 412. At420, the fusion component can fuse the first and second subset of pixelsto generate a fused CT image 422.

Process 400 requires the projection space filtering component 312 tohave access to the original projection data (P) 402 acquired from a CTscan. In some implementations, this original projection data may not beavailable. For example, in some implementation, the system 300 may onlyhave access to original CT images previously reconstructed using astandard or conventional CT image reconstruction processes. In someimplementations of these embodiments when only original CT images areavailable, the image data processing component 310 can process theoriginal CT image using a forward-projection processes (e.g., viaprojection component 320) to generate simulated or estimated projectiondata for the original CT image. Thereafter, the projection spacefiltering component 312 can perform the projection space filteringprocess on the simulated projection data to generate the streak freeimage I_(s). In particular, the projection component 320 can forwardproject the pixels of the original CT image to estimate the projectionsp(col,row,view) for the different views and channels, and estimate thepathlengths for the simulated projections, as illustrated in FIG. 5 .

In this regard, FIG. 5 illustrates forward projection of a CT image toobtain simulated projection data in accordance with one or moreembodiments of the disclosed subject matter. In FIG. 5 , area 502corresponds to the original CT image, point 504 corresponds to the 2D(x, y) coordinates of one pixel of the CT image, arrowed line 508corresponds to the direction for a projection pass from (x,y) at a knownview angle (j) and channel (i), and line 510 corresponds to a relativeposition of the detector array of the CT imaging system used to generatethe original CT image. With reference to FIGS. 3 and 5 , the projectioncomponent 320 can forward project the original CT image (i.e., thestreak image I) for each pixel in the image and for each of thedifferent view angles (n) to get simulated projections p(i,j),j∈[1 n]and i channel index. The forward projection means the integration of thepixel's CT value along arrowed line 508. In this regard, each of thepixels in an CT image have a CT value that denotes the attenuation levelfor that pixel, wherein the higher the CT value the higher the XRattenuation capability/capacity of that pixel. The integration of the CTvalue for the pixel corresponding to point 504 along arrowed line 508can be used to calculate the XR attenuation along the line and used asthe simulated projection value for its corresponding projection p,wherein the higher the integration value the longer pathlength. Thepathlength can be determined as a function of the simulated projectionvalue divided by the water attenuation coefficient (i.e., p/μ_(w)). Thenoise for each of the simulated projections can also be estimated usingthe techniques described with reference to the projection spacefiltering process (e.g., using the look-up table).

FIG. 6 presents a flow diagram of another example computer-implementedprocess 600 for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter. Process 600 is similarto process 400 yet incorporates forward projection of an original CTimage to obtain simulated projection data from which the prefilteredprojection data is generated. Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

With reference to FIGS. 3 and 6 , in accordance with process 600, at604, the projection component can forward project a first CT image 602(i.e., the streak image I) to generate simulated projection data (Ps)606 (as illustrated in FIG. 5 ). At 608 the projection filteringcomponent 312 can then perform the projection space filtering processesat 608 on the simulated projection data (Ps) 606 to filter the simulatedprojection data into prefiltered projection data (P_(f)) 610. At 612,the reconstruction component 314 can reconstruct a second CT image 614(e.g., the streak free image I_(s)) from the prefiltered projection data(P_(f)). At 614, the fusion component 316 can identify and extract afirst subset of pixels 618 from one or more non-uniform areas in thefirst CT image 602 and a second subset of pixels 620 from one or moreuniform areas in the second CT image 614. At 622, the fusion component316 can fuse the first and second subset of pixels to generate a fusedCT image 624.

With reference again to FIGS. 3 and 5 , in other embodiments when onlyoriginal CT images are available and the original projection data P forthe CT images is not available, the image data processing component 310can process the original CT image using image space filtering (e.g., viamage space filtering component 318) to generate the streak free imageI_(s). With these embodiments, the projection component 320 can forwardproject the original CT image as illustrated in FIG. 5 to generate thesimulated projections for the original CT image, the estimatedpathlengths for the simulated projections and the noise levels for thesimulated projections. For each pixel (x,y) in the original CT image(i.e., image I), the image space filtering component 518 can filter therespective pixels for each view (j). In this regard, with reference toFIG. 5 , arrowed line 508 indicates the direction of for projection pass(x,y) at the view j. The image space filtering component 318 applies thefilter along the direction perpendicular to the projection (i.e.,perpendicular to line 506), and the convolution kernel K is determinedbased on the estimated noise level associated with each projection(e.g., in accordance with the techniques described with reference to theprojection space filtering processes). In this regard, the image spacefiltering component 318 filters each pixel (x,y) for each view andsummed together, divided by n (the number of view). With this method,the respective pixel (x,y) will be filtered stronger at the views withlonger pathlengths and less (or not at all) for the view with shorterpathlengths. It will be filtered less or not filtered at view with shortpath length. After all pixels in the original CT image I are filtered,the resulting image becomes the streak free image I_(s). Thereafter, thefusion component 316 can perform the image fusion/blending process usingthe original image I and the streak free image I_(s) as described above.

FIG. 7 presents a flow diagram of another example computer-implementedprocess 700 for reducing streaks in a CT image in accordance with one ormore embodiments of the disclosed subject matter. 700 is similar toprocesses 400 and 600 with the modification of usage of image spacefiltering to generating the streak free image I_(s). Repetitivedescription of like elements employed in respective embodiments isomitted for sake of brevity.

With reference to FIGS. 3 and 7 , in accordance with process 700, at704, the image space filtering component 318 can perform image spacefiltering on a first CT image 702 (i.e., the streak image I) to generatethe second CT image 706 (i.e., the streak free image I_(s)). At 708 thefusion component 316 can identify and extract a first subset of pixels710 from one or more non-uniform areas in the first CT image 702 and asecond subset of pixels 712 from one or more uniform areas in the secondCT image 706. At 714, the fusion component 316 can fuse the first andsecond subset of pixels to generate a fused CT image 716.

FIG. 8 presents another example computing system 800 that facilitatesreducing streaks in CT images in accordance with one or more embodimentsof the disclosed subject matter. System 800 is similar to system 300with the addition of training component 802, training data preparationcomponent 804 and inferencing component 806 to the computing device 308.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

In accordance with system 800, the fused CT image data 328 generatedusing one or more of the techniques described above (e.g., process 200,process, 400, process 600 and/or process 700) can be added to themedical image database 302 (or another suitable database) and used totrain and develop one or more image transformation models using machinelearning techniques (e.g., via the training component 802) toautomatically reduce streaks in a given input CT image. In this regard,one or more of the methods for generating fused images described abovecan also be used to prepare a training data set for training (e.g., viathe training component 802) a deep neural network model to generate anoptimized image corresponding to the fused image described above givenan original CT image (e.g., processed using a standard reconstructionprocesses) as input.

For example, with reference to FIGS. 2 and 8 , the training dataset cancomprise pairs CT images, wherein each pair comprises an original CTimage reconstructed using the standard reconstruction processcorresponding to the first image in process 200, and a fused CT imagecorresponding to the third image in process 200. The process used togenerate the third image in each pair can include process 400, process600, process 700, or combinations thereof). In some embodiments, thetraining process can comprise training the neural network (e.g., via thetraining component 804) to transform the original CT images intocorresponding versions of their paired fused images. For example, insome embodiments, the neural network can comprise a transformer network(an image transformation model) comprising an encoder network, a latentspace, and a decoder network. The training component 802 can train theencoder network to encode the original CT image into a latent spacerepresentation thereof and train the decoder network to decode thelatent space representation into the paired fused version of theoriginal CT image. After the image transformation model has beentrained. The inferencing component 806 can apply the trained imagetransformation model to a new CT image comprising streaks (e.g., a CTimage include in the original CT image data 306), and the output of theimage transformation model will comprise an optimized image with reducedstreaks corresponding to fused CT image data 328.

Additionally, or alternatively, the training process can comprisetraining (e.g., via the training component 802) the neural network modelto estimate streak data included in the original CT images, wherein thestreak data used for the training process can be calculated based ondifference between the original CT images and their paired fused images.With these embodiments, the training data preparation component 804 cangenerate the streak data for each pair of images in the training datasetbased on differences between the original CT images and their pairedfused images. With these embodiments, the output of the trained neuralnetwork comprises predicted streak data for a given original CT image.After the network has been trained, the trained neural network, theinferencing component 806 can applied the trained network to a neworiginal CT image (e.g., with streaks) to generate estimated streak datafor the new CT image. The inferencing component 806 can further generatea final optimized CT image with reduced streaks by removing theestimated streak data (output by the neural network model) from theoriginal CT image.

FIG. 9 presents a flow diagram of an example computer-implementedprocess 900 for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter. Process 900 corresponds to an end-to-end process thatcan be performed by system 800 using the training component 804, and theinferencing component 808. Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

Process 900 involves a model training phase in which an imagetransformation model 908′ is trained (e.g., via training component 802)on the training dataset 902. As noted above, the training dataset 902can comprise a plurality of pairs of images, wherein each image paircomprises a streak image corresponding to the first image in process 200and a fused image (I_(f)) corresponding to the third image in process200. In this regard, at 904, the training component 802 can train theimage transformation model 908′ to transform the first images into theirpaired third images (the fused images) using one or more machinelearning techniques (e.g., supervised training, unsupervised training,semi-supervised training, and the like). The type of the imagetransformation model 908′ can vary. For example, the imagetransformation model 908′ can comprises one or more deep learningmodels, one or more neural network models, deep neural network models(DNNs), one or more convolutional neural network models (CNNs), one ormore generative adversarial neural network models (GANs), one or morelong short-term memory models (LSTMs), one or more attention-basedmodels, one or more transformers, and the like. The image transformationmodel 908′ is denoted with an asterisk′ to indicate the model is not yettrained and/or undergoing the model training phase and without anasterisk′ to indicate the model has completed training. In this regard,the output of the training phase includes a trained version of the imagetransformation model 908. At 910, the inferencing component can applythe trained image transformation model 908 to a new CT image 912 togenerate an optimized CT image 914 that has a reduced level of streakscompared to the new image 912.

FIG. 10 presents a flow diagram of another example computer-implementedprocess 1000 for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter. Process 1000 corresponds to another ML model trainingprocess that can be performed by the training component 802 and thetraining data preparation component 804 of system 800. Repetitivedescription of like element employed in respective embodiments isomitted for sake of brevity.

Process 1000 involves a training data preparation phase 1004 and a modeltraining phase 1012. In accordance with the training data preparationphase 1004, at 1002 the training data preparation component 804 canprocess the training data 902 to determine streak data 1006 for eachtraining image pair. The streak data 1006 corresponds to the streakimage data removed from the first images. In various embodiments, thetraining data preparation component 804 can determine/generate thestreak data for each pair based on differences between the first imagesand the third images (e.g., the first image I minus the third imageI_(f)).

At 1008, the training component 802 can then perform the training phase1012 and train a deep learning network 1010′ to estimate the streak data1006 for the respective first images. In this regard, the input to thedeep learning network 1010′ comprises the first image and the outputcomprises the estimated streak data 1006. The deep learning network1010′ is denoted with an asterisk′ to indicate the model is not yettrained and/or undergoing the model training phase and without anasterisk′ to indicate the model has completed training. In this regard,the output of the training phase 1012 includes a trained version of thedeep learning network 1010 configured to receive an original CT image asinput and estimate the steak data included in that image.

FIG. 11 presents a flow diagram of another example computer-implementedprocess 1100 for reducing streaks in CT images using machine learningtechniques in accordance with one or more embodiments of the disclosedsubject matter. Process 1100 corresponds to an inferencing phase ormodel application phase that can be performed by the inferencingcomponent 806 using the trained version of the deep learning network1010. In accordance with process 1100, at 1102, the inferencingcomponent 806 can apply the trained deep learning network 1010 to a newCT image 1104 to generate estimated streak data 1106 for the new CTimage 1104. At 1108, the inferencing component 806 can remove theestimate streak data from the new CT image 1104 to generate an optimizedCT image 1110 that has a reduced level of streaks compared to the newimage 1104.

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, procedural programminglanguages, such as the “C” programming language or similar programminglanguages, and machine-learning programming languages such as like CUDA,Python, Tensorflow, PyTorch, and the like. The computer readable programinstructions can execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server using suitable processing hardware. In the latterscenario, the remote computer can be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection can be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In various embodiments involving machine-learning programminginstructions, the processing hardware can include one or more graphicsprocessing units (GPUs), central processing units (CPUs), and the like.For example, one or more of the disclosed machine-learning models (e.g.,the image transformation model 908, the deep learning network 1010and/or combinations thereof) may be written in a suitablemachine-learning programming language and executed via one or more GPUs,CPUs or combinations thereof. In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 12 , the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), or the like. Further, the order in which some or all of theprocess blocks appear in each process should not be deemed limiting.Rather, it should be understood that some of the process blocks can beexecuted in a variety of orders, not all of which can be explicitlyillustrated herein.

With reference to FIG. 12 , an example environment 1200 for implementingvarious aspects of the claimed subject matter includes a computer 1202.The computer 1202 includes a processing unit 1204, a system memory 1206,a codec 1235, and a system bus 1208. The system bus 1208 couples systemcomponents including, but not limited to, the system memory 1206 to theprocessing unit 1204. The processing unit 1204 can be any of variousavailable processors. Dual microprocessors, one or more GPUs, CPUs, andother multiprocessor architectures also can be employed as theprocessing unit 1204.

The system bus 1208 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1206 includes volatile memory 1210 and non-volatilememory 1212, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 1202, such as during start-up, is stored innon-volatile memory 1212. In addition, according to present innovations,codec 1235 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec1235 is depicted as a separate component, codec 1235 can be containedwithin non-volatile memory 1212. By way of illustration, and notlimitation, non-volatile memory 1212 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 1212 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 1212 can be computer memory (e.g., physically integrated withcomputer 1202 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 1210 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 1202 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 12 illustrates, forexample, disk storage 1214. Disk storage 1214 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 1214 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage 1214 to thesystem bus 1208, a removable or non-removable interface is typicallyused, such as interface 1216. It is appreciated that disk storage 1214can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 1236) of the types of information that are stored todisk storage 1214 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 1228).

It is to be appreciated that FIG. 12 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1200. Such software includes anoperating system 1218. Operating system 1218, which can be stored ondisk storage 1214, acts to control and allocate resources of thecomputer 1202. Applications 1220 take advantage of the management ofresources by operating system 1218 through program modules 1224, andprogram data 1226, such as the boot/shutdown transaction table and thelike, stored either in system memory 1206 or on disk storage 1214. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1202 throughinput device(s) 1228. Input devices 1228 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1204through the system bus 1208 via interface port(s) 1230. Interfaceport(s) 1230 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1236 usesome of the same type of ports as input device(s) 1228. Thus, forexample, a USB port can be used to provide input to computer 1202 and tooutput information from computer 1202 to an output device 1236. Outputadapter 1234 is provided to illustrate that there are some outputdevices 1236 like monitors, speakers, and printers, among other outputdevices 1236, which require special adapters. The output adapters 1234include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1236and the system bus 1208. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 1238.

Computer 1202 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1238. The remote computer(s) 1238 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1202. For purposes of brevity, only a memory storage device 1240 isillustrated with remote computer(s) 1238. Remote computer(s) 1238 islogically connected to computer 1202 through a network interface 1242and then connected via communication connection(s) 1244. Networkinterface 1242 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1244 refers to the hardware/softwareemployed to connect the network interface 1242 to the bus 1208. Whilecommunication connection 1244 is shown for illustrative clarity insidecomputer 1202, it can also be external to computer 1202. Thehardware/software necessary for connection to the network interface 1242includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationscan be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: an image processing componentthat obtains a pair of computed tomography images reconstructed from asame set of projection data, the pair comprising a first imagereconstructed from the projection data using a standard reconstructionprocess and a second image reconstructed from the projection data usinga filtering reconstruction process that results in the second imagecomprising a first reduced level of streaks relative to the first image;and a fusion component that generates a third image by fusing a firstsubset of pixels extracted from one or more non-uniform areas in thefirst image and a second subset of pixels extracted from one or moreuniform areas in the second image, wherein the third image comprises asecond reduced level of streaks relative to the first image.
 2. Thesystem of claim 1, wherein the fusion component determines noise levelmeasures for each pixel of the second image and identifies the firstsubset of pixels and the second subset of pixels based on the noiselevel measures and one or more defined threshold values for the noiselevel measures of the second subset.
 3. The system of claim 1, whereinthe standard reconstruction process comprises reconstructing the firstimage from the projection data without prefiltering the projection dataand wherein the filtering reconstruction processes comprises filteringthe projection data into prefiltered projection data and reconstructingthe second image from the prefiltered projection data, wherein theprefiltered projection data comprises projections with reduced noise asfunction of pathlengths of the projections or function of a combinationof the pathlengths and tube current, and wherein an amount of thereduced noise increases as the pathlengths increase or the tube currentdecreases.
 4. The system of claim 3, wherein the computer executablecomponents further comprise: a projection component that estimates theprojection data from the first image using a forward image projectionprocess, resulting in simulated projection data, and wherein thefiltering reconstruction processes comprises filtering the simulatedprojection into the prefiltered projection data as opposed to thefiltering the projection data.
 5. The system of claim 3, wherein thecomputer executable components further comprise: a projection spacefiltering component that generates the prefiltered projection data fromthe projection data, wherein the projection space filtering componentestimates the pathlengths of the projections using a projectionsmoothing process, estimates noise levels of the projections based onthe pathlengths or a combination of the pathlengths and tube current,and determines the amount of the reduced noise for the projections basedon the noise levels.
 6. The system of claim 5, wherein the computerexecutable components further comprise: a reconstruction component thatgenerates the second image from the prefiltered projection data.
 7. Thesystem of claim 1, wherein the computer executable components furthercomprise: a training component that receives a plurality of pairs ofcomputed tomography images respectively comprising original imagesgenerated using the standard reconstruction process and fused imagescorresponding to the third image generated by the fusion component andtrains an image transformation model comprising a neural network totransform the original images into corresponding versions of the fusedimages, resulting in a trained image transformation model.
 8. The systemof claim 7, wherein the computer executable components further comprise:an inferencing component that receives a new computed tomography imagereconstructed from a new set of projection data using the standardreconstruction process and generates an optimized image with a reducedlevel of streaks relative to the new computed tomography images usingthe trained image transformation model.
 9. The system of claim 7,wherein the computer executable components further comprise: a trainingdata preparation component that determines streak data for each pair ofthe plurality of pairs based on differences between the original imagesand the fused images, and wherein the training component trains theneural network to estimate the streak data given the original images asinput, resulting in estimated streak data, and wherein the imagetransformation model removes the estimated streak data from the originalimages to generate the corresponding versions of the fused images. 10.The system of claim 1, wherein the filtering reconstruction processcomprises an image space filtering processes and wherein the computerexecutable components further comprise: an image space filteringcomponent that performs the image space filtering process on the firstimage to generate the second image.
 11. A method, comprising: obtaining,by a system comprising a processor, a pair of computed tomography imagesreconstructed from a same set of projection data, the pair comprising afirst image reconstructed from the projection data using a standardreconstruction process and a second image reconstructed from theprojection data using a filtering reconstruction process that results inthe second image comprising a first reduced level of streaks relative tothe first image; and generating, by the system, a third image by fusinga first subset of pixels extracted from one or more non-uniform areas inthe first image and a second subset of pixels extracted from one or moreuniform areas in the second image, wherein the third image comprises asecond reduced level of streaks relative to the first image.
 12. Themethod of claim 11, further comprising: determining, by the system,noise level measures for each pixel of the second image; andidentifying, by the system, the first subset of pixels and the secondsubset of pixels based on the noise level measures and one or moredefined threshold values for the noise level measures of the secondsubset.
 13. The method of claim 11, wherein the standard reconstructionprocess comprises reconstructing the first image from the projectiondata without prefiltering the projection data and wherein the filteringreconstruction processes comprises filtering the projection data intoprefiltered projection data and reconstructing the second image from theprefiltered projection data, wherein the prefiltered projection datacomprises projections with reduced noise as function of pathlengths ofthe projections or function of a combination of the pathlengths and tubecurrent, and wherein an amount of the reduced noise increases as thepathlengths increase or the tube current decreases.
 14. The method ofclaim 13, further comprising: estimating, by the system, the projectiondata from the first image using a forward image projection process,resulting in simulated projection data, and wherein the filteringreconstruction processes comprises filtering the simulated projectioninto the prefiltered projection data as opposed to the filtering theprojection data.
 15. The method of claim 13, further comprisingperforming the prefiltering projection process by the system,comprising: estimating, by the system, the pathlengths of theprojections using a projection smoothing process; estimating, by thesystem, noise levels of the projections based on the pathlengths or acombination of the pathlengths and tube current; determining, by thesystem, the amounts of the reduced noise for the projections based onthe noise levels; and generating, by the system, the prefilteredprojection data from the projection data by removing the amounts of thereduced noise from the projections.
 16. The method of claim 15, whereinthe computer executable components further comprise: generating, by thesystem, the second image from the prefiltered projection data.
 17. Themethod of claim 11, further comprising: receiving, by the system, aplurality of pairs of computed tomography images respectively comprisingoriginal images generated using the standard reconstruction process andfused images corresponding to the third image generated by the fusioncomponent; and training, by the system, an image transformation modelcomprising a neural network to transform the original images intocorresponding versions of the fused images, resulting in a trained imagetransformation model.
 18. The method of claim 17, further comprising:receiving, by the system, receives a new computed tomography imagereconstructed from a new set of projection data using the standardreconstruction process; and generating, by the system, an optimizedimage with a reduced level of streaks relative to the new computedtomography images using the trained image transformation model.
 19. Thesystem of claim 17, wherein the computer executable components furthercomprise: determining, by the system, streak data for each pair of theplurality of pairs based on differences between the original images andthe fused images, and wherein the training comprises training the neuralnetwork to estimate the streak data given the original images as input,resulting in estimated streak data, and wherein the image transformationmodel removes the estimated streak data from the original images togenerate the corresponding versions of the fused images.
 20. The methodof claim 11, wherein the filtering reconstruction process comprises animage space filtering processes and wherein the method furthercomprises: performing, by the system, the image space filtering processon the first image to generate the second image.
 21. A non-transitorymachine-readable storage medium, comprising executable instructionsthat, when executed by a processor, facilitate performance ofoperations, comprising: obtaining a pair of computed tomography imagesreconstructed from a same set of projection data, the pair comprising afirst image reconstructed from the projection data using a standardreconstruction process and a second image reconstructed from theprojection data using a filtering reconstruction process that results inthe second image comprising a first reduced level of streaks relative tothe first image; and generating a third image by fusing a first subsetof pixels extracted from one or more non-uniform areas in the firstimage and a second subset of pixels extracted from one or more uniformareas in the second image, wherein the third image comprises a secondreduced level of streaks relative to the first image.
 22. Thenon-transitory machine-readable storage medium of claim 21, wherein theoperations further comprise: determining noise level measures for eachpixel of the second image; and identifying the first subset of pixelsand the second subset of pixels based on the noise level measures andone or more defined threshold values for the noise level measures of thesecond subset.